Background Mendelian randomization has been previously used to estimate the effects of binary and ordinal categorical exposures—e.g. Type 2 diabetes or educational attainment defined by qualification—on outcomes. Binary and categorical phenotypes can be modelled in terms of liability—an underlying latent continuous variable with liability thresholds separating individuals into categories. Genetic variants influence an individual’s categorical exposure via their effects on liability, thus Mendelian-randomization analyses with categorical exposures will capture effects of liability that act independently of exposure category. Methods and results We discuss how groups in which the categorical exposure is invariant can be used to detect liability effects acting independently of exposure category. For example, associations between an adult educational-attainment polygenic score (PGS) and body mass index measured before the minimum school leaving age (e.g. age 10 years), cannot indicate the effects of years in full-time education on this outcome. Using UK Biobank data, we show that a higher educational-attainment PGS is strongly associated with lower smoking initiation and higher odds of glasses use at age 15 years. These associations were replicated in sibling models. An orthogonal approach using the raising of the school leaving age (ROSLA) policy change found that individuals who chose to remain in education to age 16 years before the reform likely had higher liability to educational attainment than those who were compelled to remain in education to age 16 years after the reform, and had higher income, lower pack-years of smoking, higher odds of glasses use and lower deprivation in adulthood. These results suggest that liability to educational attainment is associated with health and social outcomes independently of years in full-time education. Conclusions Mendelian-randomization studies with non-continuous exposures should be interpreted in terms of liability, which may affect the outcome via changes in exposure category and/or independently.

中文翻译：

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解释分类暴露影响的孟德尔随机估计，例如疾病状态和教育程度

背景 孟德尔随机化以前曾用于估计二元和有序分类暴露（例如 2 型糖尿病或由资格定义的教育程度）对结果的影响。二元和分类表型可以根据责任进行建模 - 一个潜在的潜在连续变量，具有将个人分成类别的责任阈值。遗传变异通过其对责任的影响影响个人的分类暴露，因此具有分类暴露的孟德尔随机化分析将捕捉独立于暴露类别的责任的影响。方法和结果 我们讨论了如何使用类别暴露不变的组来检测独立于暴露类别的责任效应。例如，成人教育程度多基因评分 (PGS) 与最低离校年龄（例如 10 岁）之前测量的体重指数之间的关联不能表明全日制教育年限对这一结果的影响。使用英国生物银行的数据，我们表明，受教育程度较高的 PGS 与 15 岁时较低的吸烟开始和较高的眼镜使用几率密切相关。这些关联在兄弟模型中被复制。使用提高离校年龄 (ROSLA) 政策变化的正交方法发现，在改革前选择继续接受教育至 16 岁的个人可能比那些被迫继续接受教育至年龄的人更容易受到教育程度的影响改革16年后，收入更高，吸烟量更低，成年后使用眼镜的几率更高，剥夺的几率更低。这些结果表明，受教育程度的责任与健康和社会成果相关，与全日制教育年限无关。结论 非连续暴露的孟德尔随机研究应根据责任进行解释，这可能通过改变暴露类别和/或独立地影响结果。